



"""simple docstring"""





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__lowerCAmelCase							:       Optional[int]          =       logging.get_logger(__name__)



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							['''pixel_values''']





		def __init__(       self				,     _lowercase = True				,     _lowercase = 1 / 2_5_5				,     _lowercase = True				,     _lowercase = 8				,     **_lowercase				,     )			->    None:







								'''simple docstring'''

								super().__init__(**_lowercase )

								snake_case_ :			Any            =      do_rescale
								snake_case_ :			List[str]            =      rescale_factor
								snake_case_ :			Optional[int]            =      do_pad
								snake_case_ :			List[str]            =      pad_size





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase )			->    np.ndarray:







								'''simple docstring'''

								return rescale(_lowercase				,     scale=_lowercase				,     data_format=_lowercase				,     **_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = None )			->    List[Any]:







								'''simple docstring'''

								snake_case_      ,		snake_case_ :			Any            =      get_image_size(_lowercase )
								snake_case_ :			List[str]            =      (old_height // size + 1) * size - old_height
								snake_case_ :			int            =      (old_width // size + 1) * size - old_width

								return pad(_lowercase				,     ((0, pad_height), (0, pad_width))				,     mode="""symmetric"""				,     data_format=_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = ChannelDimension.FIRST				,     **_lowercase				,     )			->    Any:







								'''simple docstring'''

								snake_case_ :			str            =      do_rescale if do_rescale is not None else self.do_rescale
								snake_case_ :			Optional[Any]            =      rescale_factor if rescale_factor is not None else self.rescale_factor
								snake_case_ :			Union[str, Any]            =      do_pad if do_pad is not None else self.do_pad
								snake_case_ :			int            =      pad_size if pad_size is not None else self.pad_size

								snake_case_ :			Optional[Any]            =      make_list_of_images(_lowercase )

								if not valid_images(_lowercase ):
														raise ValueError(
														    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
														    """torch.Tensor, tf.Tensor or jax.ndarray.""" )

								if do_rescale and rescale_factor is None:
														raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )

								# All transformations expect numpy arrays.
								snake_case_ :			Optional[int]            =      [to_numpy_array(_lowercase ) for image in images]

								if do_rescale:
														snake_case_ :			Any            =      [self.rescale(image=_lowercase				,     scale=_lowercase ) for image in images]

								if do_pad:
														snake_case_ :			Union[str, Any]            =      [self.pad(_lowercase				,     size=_lowercase ) for image in images]

								snake_case_ :			int            =      [to_channel_dimension_format(_lowercase				,     _lowercase ) for image in images]

								snake_case_ :			str            =      {"""pixel_values""": images}
								return BatchFeature(data=_lowercase				,     tensor_type=_lowercase )







"""simple docstring"""




import warnings

from .generation import TFGenerationMixin






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				# warning at import time
				warnings.warn(
				    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
				    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,   UpperCAmelCase__		,   )



